Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

Li Ding; Li Mao; Xiao Feng Wang

doi:10.4028/www.scientific.net/AMM.687-691.2693

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Classification of P2P Traffic Based on a...

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

Abstract:

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

2693-2697

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.2693

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Online since:

November 2014

Authors:

Li Ding*, Li Mao, Xiao Feng Wang

Keywords:

Ensemble Classifier, Feature Selection, Machine Learning, P2P Traffic

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* - Corresponding Author

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